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Tag: active

  • Efficient Evaluation of LLM Performance with Statistical Guarantees

    Efficient Evaluation of LLM Performance with Statistical Guarantees arXiv:2601.20251v1 Announce Type: new Abstract: Exhaustively evaluating many large language models (LLMs) on a large suite of benchmarks is expensive. We cast benchmarking as finite-population inference and, under a fixed query budget, seek tight confidence intervals (CIs) for model accuracy with valid frequentist coverage. We propose Factorized…

  • Gradient-based Active Learning with Gaussian Processes for Global Sensitivity Analysis

    Gradient-based Active Learning with Gaussian Processes for Global Sensitivity Analysis arXiv:2601.11790v1 Announce Type: new Abstract: Global sensitivity analysis of complex numerical simulators is often limited by the small number of model evaluations that can be afforded. In such settings, surrogate models built from a limited set of simulations can substantially reduce the computational burden, provided…

  • Memorize Early, Then Query: Inlier-Memorization-Guided Active Outlier Detection

    Memorize Early, Then Query: Inlier-Memorization-Guided Active Outlier Detection arXiv:2601.10993v1 Announce Type: new Abstract: Outlier detection (OD) aims to identify abnormal instances, known as outliers or anomalies, by learning typical patterns of normal data, or inliers. Performing OD under an unsupervised regime-without any information about anomalous instances in the training data-is challenging. A recently observed phenomenon,…

  • Robust Sampling for Active Statistical Inference

    Robust Sampling for Active Statistical Inference arXiv:2511.08991v1 Announce Type: new Abstract: Active statistical inference is a new method for inference with AI-assisted data collection. Given a budget on the number of labeled data points that can be collected and assuming access to an AI predictive model, the basic idea is to improve estimation accuracy by…

  • Active Subspaces in Infinite Dimension

    Active Subspaces in Infinite Dimension arXiv:2510.11871v1 Announce Type: new Abstract: Active subspace analysis uses the leading eigenspace of the gradient’s second moment to conduct supervised dimension reduction. In this article, we extend this methodology to real-valued functionals on Hilbert space. We define an operator which coincides with the active subspace matrix when applied to a…

  • Active Learning for Manifold Gaussian Process Regression

    Active Learning for Manifold Gaussian Process Regression arXiv:2506.20928v1 Announce Type: new Abstract: This paper introduces an active learning framework for manifold Gaussian Process (GP) regression, combining manifold learning with strategic data selection to improve accuracy in high-dimensional spaces. Our method jointly optimizes a neural network for dimensionality reduction and a Gaussian process regressor in the…

  • Efficient Human-in-the-Loop Active Learning: A Novel Framework for Data Labeling in AI Systems

    Efficient Human-in-the-Loop Active Learning: A Novel Framework for Data Labeling in AI Systems arXiv:2501.00277v1 Announce Type: new Abstract: Modern AI algorithms require labeled data. In real world, majority of data are unlabeled. Labeling the data are costly. this is particularly true for some areas requiring special skills, such as reading radiology images by physicians. To…